Good afternoon, everyone, or good morning, depending on where you're at. I want to welcome you all to our webinar today. We are looking forward to hearing from Mike Trelo. He comes to us as a wealth of experience. For those of you that don't know me, my name is Dawn McEwen. I'm the trauma program manager at Baylor Scott & White in Temple, Texas, and I am also the education chair this year for TCAA, so very excited to be able to have Mike come to us to talk to us about our data and all that goes with it. He comes to us with a wealth of experience. He's been a trauma registrar and analyst for 35 years. He's worked for a trauma registry vendor for level one and two adult and pediatric trauma centers. He created the basic and advanced trauma registrar course for a trauma education company, and thank you. He also served in our Air Force as a firefighter rescue and paramedic, and he's now working for Brundage Group as a director of trauma and quality, where he created the trauma tip cards for trauma registrars to use. So welcome, Mike. We look forward to hearing all the information you have to share with us. Thank you, Dawn. All right. Welcome, everybody. Like she said, I'm still a dual-certified registrar. I still am working for a hospital. I do a couple hours every day for Brooke Army Medical Center, so shout out to them. That's my fun time. That's my play time. I still keep my hand in it doing charts and stuff, and that's my passion. What I want to talk about today is your trauma-dated New Year resolution, and as we go into it, I have no financial relationship to disclose, and all the information is there for the CEs. To look at some of the objectives, these are some of the things that we're going to be looking at. We're going to take a look at types of data and their roles, how to streamline your data, how to prepare your data for download to TQIP, and then we're going to look at where does your data go once it leaves the hospital, and everyone says, well, it goes to TQIP, but it goes way beyond that. So starting out, this is one of my favorite pictures ever of trauma that is about to happen, and this is how patients do tend to get injured, and I think if I had a bull chasing me, I too could also run on water. But people get injured in all sorts of ways, and another favorite picture of mine is I would like to see the picture of the guy second from the left who's got the picture of the guy's face realizing he just screwed up. So as we know in trauma, people get injured in a multitude of ways, and that data that we collect is what we're going to address. Now, what is data? Well, data as the breakdown, the definition, really is information used as a basis for reasoning, discussion, calculation, or any information in a digital form that can be transmitted or processed. That's it. That's all what data really is. Now, let's talk about what's data got to do with it. Well, as we know, data is knowledge, and knowledge is leverage and power. So if we think about this, quality accurate data tells the story of who you are. Now, it's important to know who you are and where you've been so you know and you can decide where you want to go, and data is that currency that measures you against the others, and it assists in obtaining resources for your trauma program, your facility, or your community. Now, what's interesting is there is some stuff being trying to get into Congress where, you know, all these companies sell our data, and there has been a push that says, hey, they're taking our data that we've collected, they gather it, and they sell it off, and there's a big push now that we should get paid for the data that they're using. So that'd be interesting to kind of keep my eye on that one. I'm waiting to see how that goes, but data is knowledge, and knowledge is the power that we have and the leverage that we need. So I want to kind of go through some types of data as kind of as a quick refresher and just let you know that there is a qualitative type of data, and some of this can be what's called nominal data, and that's data that can be categorized by name or label, such as your gender, your hair color, color of your eyes, okay? We can put a name or label to it. That's nominal data. So we have another type of qualitative data, and this is called ordinal data, and this is data that can be categorized and ordered by some criterion, such as the patient satisfaction ratings, everybody's favorite, press Ganey scores, right, academic grades. These are, they can be ordered and categorized, okay? Another type of data that we have is what's called quantitative data, and this is, one type is discrete, and this is data that can only take certain values that are separate and distinct, like the number of trauma data professionals you have on staff or the number of PI people you have on staff. So they're separate and distinct values, okay? We have data that we can track that way. We can take a look at continuous data, and that's data that can take any value within range or interval, such as your weight and your height that they want for BMI, okay? So these are just some basic types of data that we can deal with. Now, what I want to do is take a look at the types of data fields. Now, this is not a complete list, but there's different types of data fields, and in data management, you need to know what it is, depends on what kind of field it is, I know what needs to go into it. So we have alphanumeric, we have date-time, we have numeric, we have lists, we have document, we have Boolean, where you can go in and type in certain things to pull that data a little bit further. There's some other types of data fields that aren't listed in here, such as calculated fields. And in the registry, there are calculated fields. It'll take this field and this field, and it'll calculate the difference, or so whatever the algorithm is behind it, to come up with stuff like your survival probability, things like that. So these are types of data fields. One interesting type of data field is, if you are using a statistical field, you cannot put a zero in there. If you put a zero in a statistical field, it will skew the data. So usually at that point, you have to put in NA or unknown, usually just NA. But there are some types of data fields that can cause problems. Statistical field is one. If you don't put it in right, it can skew your data. So with that in mind, I want to go through and just take a second to kind of absorb this role of data. While you read the cartoon, I'm getting a drink. All right. So unlike Calvin mastering the skill of the yo-yo, data actually has real application in life. So let's take a look at some of the applications that it has. Let's talk about decision making. All right. Now, data helps individuals and organizations really make informed decisions rather than relying on intuition. Now this is particularly important in business settings. Now when I talk about business settings, I'm talking about the hospital. The hospital is a business. And it's important in business settings where data-driven decisions can significantly impact profitability and growth. And in the hospital, in the EMS realm, data significantly impacts the improvement of patient outcomes. So one of the ways that data impacts our life is with decision makings. Another one that it does is problem solving. Now, this is the idea that data can be used to identify problems, really understand their causes, and propose solutions. For example, a hospital is having a problem getting patient beds. And patients are being boarded in the emergency department. And because they can't get an open bed. So we've identified a problem. We can understand their cause. And we can propose a solution. But maybe the solution is simply to have more housekeeping available to clean the bed sooner. Or actually have the case managers and the doctors work with the floors to get the patients out before noon. And actually work on that. So there are ways that problem solving can be done using the identifying a problem, understanding their causes, and proposed solutions. Another way that data affects our life is by predicting and forecasting. Now, data can be used to predict future trends or outcomes. Think about the meteorologists. They use weather data to forecast future weather conditions. Now we won't discuss how bad they are or how often they're wrong. But that's the idea of predicting and forecasting. Now think about this. In your trauma registry program, you can track the day of week, the time when the traumas occur the most. And you can then use that to really kind of predict and forecast how much more staff do you need on trauma on certain times on days of the week. Now, I worked with a company that did forecasting. And they used a forecasting logic tool, the Erlanger Theorem. And they were able to predict, looking at history, how many people should be working in a call center to answer the number of calls that should come in. And with that, I actually approached the CEO of that company. I said, there's something interesting. Do you think you can use that same forecasting to look at trauma centers and to be able to kind of forecast how much do you think, predict number of traumas coming in on certain days of the week based on time, date, weather, stuff that's happened in the past? And he was intrigued by that. So I'm waiting for him to get back to me. But think about that. Using the data, I can predict and forecast by what's happened in the past, how many trauma nurses I need to put on staff on certain days of the week and maybe shifts, because certain shifts get higher volumes of trauma. So that's predicting and forecasting. Another way trauma can be used in our life is for improving processes. Everyone's favorite, the PI. So hospitals often collect data about their processes to identify inefficiencies and areas for improvement. And in our case, on the trauma side, that's what performance improvement really is. And a lot of people will tell you that the trauma registry program really is a PI program. And so the registry is used to drive the PI. We'll talk about that here in a little bit. Another way that data can be used in our life is to understanding behaviors. And in fields like psychology and marketing, data is used to understand human behaviors, patterns, and preferences. And for us, in the injury prevention side, everyone's favorite fall within the home. And you look at it, and like with my parents, they had a lot of throw rugs everywhere. And when my dad started falling, I would go in and I'd take all of the rugs and I'd explain, this is why you're tripping. You're tripping on all of these throw rugs you have everywhere. And I put them in the closet. And next time I come back to see them, they're all back on the floor again. So understanding their behavior, that was their behavior. They always had those throw rugs, and doggone it, they're going to keep them there. So data can help us understand behaviors and kind of figure out the patterns, such as people over 65 do tend to fall. We'll understand that behavior and that pattern. Another one that's used for is scientific research. And this is good on the trauma side because we're able to look at all things that's essential for research. Scientists will collect data to test their hypothesis and advance knowledge in their field. And especially in trauma, you'll see new things coming up soon. Some ideas that they're looking at, some scientific research. And I know I'm in touch. That was my playtime. I was able to work with trauma research and I loved it. I couldn't get enough of it. And being able to show that how looking at our data and looking at the research, how we can implement changes. That's a great way, another way that data affects our daily life. So with that in mind, let's go ahead and take a look at the trauma registry data to really see really what is required. Now everyone has to have, and every business has to have, if you use a database, you have to have rules of what data do you need to collect. With us, we have the algorithm here. This is the current one because you'll see it was a patient transfer discharge to hospice. So that's new in here. So this is the way, this guides us to let us know what data we need to collect. So this is one of our national standards. So we have to go through and the main thing is you really have to understand this before you start off. And I can tell you, I get calls a lot of people asking, should this patient be included in the registry? And my first thing is to tell them, what does the NTDS say? Because I don't want them going out and saying, look, some bozo named Mike Trilo gave me the wrong information. No, I'm going to direct you to go back and look at what does the standard say, the National Data Dictionary say. And if you still have questions, then let's talk about that. So this tells us what data, what patients do we need to gather? What data do we need to gather? One of the things that's really interesting as part of data dictionaries is sometimes you get into a way that we may have hierarchies. And in this case, for external cost codes, and these are some of the data rules that we have to deal with, is we look at the hierarchy of external cost codes. So in the middle, under multiple cost coding hierarchy, you will find that child and adult abuse takes precedence overall. So if I have several different methods of several different mechanisms that be on one patient, if child or adult abuse is involved in that, that goes first, that's primary. The next one is we're looking at terrorist events and terrorism. So that takes precedence after the child and adult abuse. Then we have cataclysmic events, the cold, the hurricanes, the earthquakes, the tornadoes, some cataclysmic event comes next. And then finally, of all things, is a transport accident. So sometimes our data that we collect has a certain hierarchy that we have to follow that if that's one of the mechanisms, then those will have to go first before anything else. So that's just an idea of what we have to view. And as a trauma registry professional, we get to look at all this information. And this helps us to guide us in how to collect it the proper way. Another one is the sometimes we get, like this one, the hospital procedures. And in this one, it's important to know that, hey, the ACS is really looking to make sure that we capture these things in particular. And anything with an asterisk, we only have to put in once. But what this will do is because I've actually got into hospitals and just shocked that patients with a one-day length of stay takes them three hours to gather that information. And I'm thinking, why? Why would it take you three hours for a one-day length of stay? Until we get to the hospital procedures. They were tracking every IV stick, every Band-Aid, everything, okay? Now, all right, we'll talk about that here in a little bit. But it's like, okay, the procedure codes are where we spend a majority of our time as registrars. That data has to be in there. So there's some descriptions that we have to be able to remember is look at the term essential to the diagnosis, stabilization, or treatment of the patient-specific injuries or complications is the data that we need to gather. So a lot of times I will find people are using or put in way too many procedures that really it's the idea is if I'm looking at all this data, what am I doing with this data? So these are just some ideas of the rules that we have to engage with in order to know what data needs to be collected and how it needs to be collected. Let's talk about some other important data that we have. We have all these pre-existing conditions, hospital events, and TQIP measures, okay? Currently, there are 30 total pre-existing conditions. There are, I think, 21 hospital events and another 30 TQIP measures for process of care, okay? So on top of our data collection, we have all these other things. Oh, and on top of that, we have sometimes state data that we have to track. And this is just a partial list of some of the states that have extra data that you must collect. And then you also have to look at, is there local hospital defined data that they want? Is there some kind of research that they're doing that we have to do collect extra fields on? Is that specific to local trauma identification? So with all of this dumped at us, a lot of times that's why you look out and you see your registrar with this look on their face. Oh my God, what do I do with all this stuff? How can I keep track of this? There's so much going on. Well, the simple answer really is, and this is when you get into actually now looking at your data and trying to figure out, how can I keep this monster from becoming a beast? Well, let's talk about what's called streamlining. We're going to look, talk about how can we streamline data? Now we know there are required data fields. I'm not talking about getting rid of streamlining those. Those we have to collect. So let's go ahead and talk about this pros and cons that we have of the more data, the better. So on the yes side, let's talk about this on the yes side. Yes. If I could collect all the data possible, then I can run numerous research things. I can run data and really twist it around. I can really go deep into it. But you have to understand that collecting a lot of data, sometimes more data, the better, can be a problem. Because if you think about it, the longer your registrar is in a chart, time is money. And I can tell you that I've dealt with hospitals that have had over 600, 700 data fields. They were living by the more data, the better. But they were constantly in a backlog state. Is more data the better? The purest would say yes. Now on the no side, let's talk about that. Is more data better? No. Here's why. Because I can spend hours looking at procedures. I can put in everything. I can put in needle sticks. I can put in everything that happens. And again, I am getting a backlog situation. Or do I want to just stick with the national definitions? Do I want to stick with the things that I need to keep track of? And again, we have to do a state, national, state, and local data fields. The more data, the better? No. If you personally, I think that we need to stick with just no, we don't need to collect everything. We need to collect the stuff that is important to us. The national, the state, and the local. And how do we do that? What you can do is take your registry team and print out every single screen on your registry. And in order to streamline, what you want to do is sit down and say, okay, I want to get some highlighters. And I want to highlight all of the NTDS fields. So I'm going to go through and highlight all the NTDS fields. After that, I'm going to come back with a different color. And I'm going to highlight all of the state data fields that we are required to gather. After that, I'm going to look at another color. And I'm going to pull up and highlight any local hospital data fields that they was to track. Now, once I'm done with that, I'm going to take a look to see, are there any fields that are not highlighted? And if that's the case, why are we collecting it? And this is the idea of looking to streamline that data. Because if you don't watch it, it gets out of control. And people don't understand that everything keeps being added, changed every year. And if you don't streamline it, you're going to get yourself into trouble. So the idea is all of those fields that are not highlighted, then you have to ask yourself, then why are we tracking it? If it's not needed by NTDS state or local, I should just mark those all as NA and move on. Because in the field of data management, as a data manager, if I'm looking at a registry and I see a lot of blanks, I can only assume that you just ignored that field. If I see an NA in there, I know that you've looked at that field and it's not applicable. No problem. So learn to streamline your data. Once you do that, you can start to manage and maintain good data. Because now you're not in a constant backlog state because I'm tracking too much stuff. If it's not required by NTDS state or local, why are we tracking it? Those are things, questions you have to ask yourself and discuss with your teams. Another thing that is highly recommended is also part of kind of like the looking at the hospital data dictionary. And I would tell you that you need to have your own internal hospital data dictionary. And this is an example of one that I'd worked up. And you can see that, you know, I will look at three different levels of documentation. And then I'm going to look for where is it located at in the EMR. What that allows you to do, that makes sure that everyone knows that first we go to ED visit history, the trauma flow sheet. If it's not there, I'm going to look in the physician's notes. If it's not there, I'm going to look in the nurse's notes. And oh, by the way, I'm going to go to this particular tab, folder, whatever EMR you're using. And I want to pull that data from this spot. Once you have that, there is no longer any room for discussion of where I think it might come from. We come together as a team and decide for this particular initial location, we're going to look in the EMR at this particular spot, because everyone knows I can find the same thing at five different places in the EMR. And if we're not all pulling from the same exact thing, that's where problems with the data comes in. So, if you don't have a hospital data dictionary, you need to start working on one right now, because that will help you when you have bring new staff in, then they can say, okay, here's the registry, here's your data dictionary, follow these fields, follow and it'll tell you exactly where to go to the EMR to find this data. So, there's no guesswork. Everyone knows exactly where it's coming from. And the other one that I like to do is the end of the year, or actually by October, I would do an annual review. And I would start in October. And what I would do is I would be able to review the new NTDS fields with the team. I could get feedback from the trauma team about what fields there are. And also, one of the things I would do is I had a data log that I would enter, say, okay, a new data field appeared this year for NTDS. So, I put that in my data log that started on January 1st, 2025. So, if someone comes and says, I want to know for the last 10 years about this particular data field, I can say, I'm sorry, we can only give you this year's data because we started January 1st. So, that way we know why sometimes that data is not five or ten years down the road. Also, one thing I like to do is I would look at reports and I would keep a report log. And I knew what reports were created and who they went to. And I could go back and say, okay, I've got all these fields. Am I reporting on all of these fields? Have I ever done a report on all of these fields? And I'll go through and say, okay, some fields we need to get rid of because we've never reported on it. And the biggest thing that we would do is I would go and the doctors would come and say, hey, I want to track this field. And my first response to them is no. I was the no man. No. And they're like, why not? I said, is it for a research project? No, we're not going to track it. Is it just because you want to know about it? That's not good enough. I'm not wasting our time for something that will never be reported on. And case in point, we had a orthopedic traumatologist come to us and said, we want to track the smoking for trauma patients and open fractures. And in specific, he goes, we want to track any kind of tobacco. They want to track cigars, pipes, chewing tobacco, because they were looking at a research study that showed that anybody with nicotine, no matter how they got it, it impacted the length of stay for distal lower open fractures. And this was a research project. So they came to us and said, can we add this particular field? We want to include all tobacco use. So they were able to say, yeah, we're part of a research project. Here's why we want this. And we're like, good. How long is that research project for? Two years. Good. After two years, we're going to take it off our list. So those are the kind of things in an annual review. That way, when I start on January 1st, we all know what the new fields are. The team has bought in on it. We've made any changes or deletions, or we quit tracking certain fields. We never delete a field, by the way. We just don't track it anymore. That way, we know that I'm keeping that beast small. I'm keeping it contained. I'm able to keep it in a functioning state that's easy for us to work with. Otherwise, and if you've never seen it, it's ugly when it gets out of control. So we're collecting data. We're to the point now where we've been going through, we're collecting data. So now we get to the part where they asked, show me the data. With that, sometimes the trauma registry is, if you ask the administration, they may call it a black hole program. Because as far as they know, trauma data is going in, but nothing's coming out. They're not seeing anything. So my thing is, make sure that your trauma registry is not considered a black hole by administration. Because they're paying people to put data in, but yet they're not seeing anything. So in their eyes, that's a black hole program. So what I would do is say, okay, before I even send this data out, I've got data. Is it clean? So we look at data validation, IRRs. I would caution you to do not go overboard on that. I've seen some pretty hairy spreadsheets that when they do validation, it'll take them hours to get through a chart. All right. Don't go overboard on that. Put that information on a shared drive. That way it allows the TPM and the TMD to see how accurate the team is. They don't have to ask you. They know they can go in and they can say, oh, look, here's our team IRR. We're at 98%. And I can look at individual IRR at that point. As long as it's on a shared drive, people have access to it. And that is also part of the written data plan that is required. Okay. So having data validation, IRRs, put it someplace where it's clean. We want to make sure the data is clean before it even goes out because nothing worse than putting data out and the medical director looks at it and goes, hmm, let me check this, double check this. And they double check and go, that's wrong. They're not going to trust your data anymore. So I will not let any data leave in a report until I know it's been cleaned, there's validation on it, we know the data is good. So let's take a look at the reporting side. I believe the reporting is the trauma data professional's responsibility. They are the ones putting the data in. And I have been running across more and more places where they are saying that they have someone else running the reports. And I'll say, do they enter the data? No. Then I would ask them, how do they know what they're looking at is accurate? As the registrar and the data professional putting that data in, when I do a report and something jumps out, I'm like, that's not right. That's not what I've been putting in. So I think it's responsibility, the trauma registrar professional's responsibility, because they put that data in, they should know how to do it. And every registrar should know how to do reporting. Someone will say, I hate it. Well, tough. That way, if I get hit by a truck tomorrow, someone can step in and keep doing the reporting for you. Let's talk about the reporting. I do not want to send any reports out unless I graph it. And here's why. I'm a big proponent of graphing. So let's all take a test. Now, everyone listen very carefully and do exactly what I tell you to do. All right. Do not, I repeat, do not think of an elephant. What did you see in your head? Did you see the word elephant spelled out? Or did you see a picture of an elephant in your brain? All right. If you saw something other than an elephant, you listen to the instructions. My point is, is that humans see in pictures. Therefore, we need to graph our reports out as much as possible. Now, I have taken in spreadsheets and, you know, I've worked a lot on these spreadsheets, five or six page spreadsheet. I've handed it to somebody, the medical director, and before I even walk out, I hear it hit the trash can. Okay. If I graph it though, they're going to look at it. Now, graph out standard reports, graph out customized reports, but I would tell you, think outside the box when you come into the data and graphing it. One thing I did, I would pull every year for the administration and they loved it, is I would get a state map with the counties in it and you can pull it off the internet. Every state has it, a map of your state with the counties. Then what I would go through by zip code and by county, I was able to show administration our catchment area. How many patients came from this county? How many patients came from this county? And the first time I put that graph up, administration said, stop, don't move. I want to see this. So, when you graph things out, do it in a way that's going to catch your attention. They've all seen that. If you pull up a graph like that and you can show them where the trauma is coming from, that's something that they've never seen before. They're going to say, okay, we need to focus more of our efforts here because last year we had X number of patients transferred into us, but it's down 20%. What's going on? So, that kind of thing when you do about reporting and graphing. Thinking about your PI and IP reports, okay, the registrars need to run the PI reports and the injury prevention reports. They put the data in. The PRQ reports, that should be gone over with the registrars. Research reports, a great deal with research and reporting. My last hospital, I got the trauma person to research, came back and she was laughing. And I said, what's so funny? She said that she was at the research group and they were looking at falls from tree stands. And they could only, in the three-year period, they found 12 patients that fell from a tree stand. Now, this was in central Wisconsin. And if you're in any kind of rural area, you know that in the fall, when the leaves fall, so do the hunters from the tree stands. So, she says, she goes, we see 10 every two or three days. And they're like, where'd you get this information? She goes, I'll bring the person the next meeting. Next meeting, I showed up and I showed them that I had created a special ICD-10 code for fall from tree stands. And because if you think about it, in the mechanism, I can look at fall from a tree, but there is no fall from a tree stand. So, I created a customized thing and we just counted how many times that would have been added to that field. So, they were like, they were asking, how did you do that? Well, it's not in ICD-10. So, I created a custom field. So, if we had a fall from a tree stand, that was entered and I can report on that. And they're like, what else do you have on your list? And I said, I can look at bar-related injuries and their eyes lit up. You can track injuries from bars? Yes, they love it. So, sometimes you have to get a little bit creative on data so that you can report on it, on things that are interested in your area. Let's take a look at where does the data go? And we're going to shift gears here. Where does the data go? Well, we know it goes to the NTDB. Right now, there's 7 to 8 million records in that. We know it goes to TEQEP, everybody's favorite docs, best box decile graph here on the left. It also goes to state, to the epidemiologist at state level. And if your state has epidemiology group, look at their website, see what kind of trauma data that they're using on the state epidemiology site. Also for state legislation, think about this, the data goes beyond that. When I was working at Barnes Hospital in St. Louis, I'd been there for a while and we got a call one day from the state of Missouri. And they asked if we could come down on a certain date and they wanted us to bring our trauma data specifically on underage drivers who were drunk. So, on a certain date, we went down with the trauma program manager and I went down, we had the data and all five level one traumas in their state showed up. And we were able, and what they were looking at is they were trying to develop a law. So what we did, we were able to show that from our hospital we had x number of people under the age of 21 who were drivers of vehicles who were drunk legally over the limit. And from that and sitting in and watching see how Budweiser was fighting against this and stuff like that, but because we were able to take this trauma data and show the state legislators, the state of Missouri passed a zero tolerance law that said if you are under 21 and you are caught with alcohol in your car, you would lose your license because they were getting too many underage people killed in or injured in drunk driving incidents. So our data can go and help pass legislation. When I saw that being done, I was blown away. I was like, this is cool. I'm actually making an impact. This information can also go to regional trauma councils. Make sure this goes to hospital administration. Once I created a quarterly hospital, a trauma hospital administration report, suddenly there was interest at the hospital administration level. In fact, all of a sudden the CEO would just show up just to say hi and see how things were going in our department. But once we got to show them we were not a black hole program that this can show information and show pertinent data and concurrent data. Next thing you know, all of a sudden we're of interest to the administration that they're now seeing data come from a program that they never saw data from before. So the data goes way past this. In fact, the data can go and it goes to levels such as this. We're going to talk about how the data is used at the CDC level and actually how NHTSA uses our trauma data. And with that, there is a website in the CDC called Whiskers. Whiskers is a great, great tool that your hospital can use. And you can use it to look at national, compare it to your state and even to your local hospital. But it looks at like fatal injuries and violence data, non-fatal injury data, leading causes of death and injury. So they're taking data submitted by us, goes to NTDB, and then CDC pulls, gets access to that data. And you can get things like this. The 10 leading causes of death in the United States, this is from 2020. But you can still see that anything in blue is unintentional injury. And still, number one, number one, from ages one through 44, trauma is the leading cause of death. And so we're looking at causes of death for both sexes, all race, ages, and all races. Now you will see that the orange, we have homicides, then we have the suicides. But you can be able to pull up data like this when you're doing some more in-depth look. Case in point, my medical director walked in one day, said, I want to know why traumas are down. And he left. And I'm thinking, okay, do I need to make him go pee in a cup? Or is there something really happening with traumas? I don't know. So I started looking around. I started looking at the trauma volume. So I ran traumas, trauma activations year over year. And I did notice that there was a slight dip in that year that he came and asked me about. So then I started digging in, what was causing that problem? Why was there suddenly some decrease in trauma? So after doing a lot more reporting and stuff, I got to the point where I thought, okay, let's think outside the box. I looked at the data for the people in the country. I was looking at the data and I pulled it by county. And I found that by county, we had a major population shift of like 20% from our surrounding counties. So I was able to see by the demographics that we had lost 20% of our population in our area over the last five years. And so I was able to pull all this together and finally say that the reason why that our traumas are down is, A, we had a statistically significant move of population. B, another trauma center opened another level two trauma center opened up 30 miles from here. And C, we need to fire our marketing people because every TV ad, radio ad, and newspaper ad is always for the other trauma center and not ours. So being able to think outside the box, I was able to take national data, look at local data, and pull it together to show, A, there has been a slight drop in trauma and here's why. So being able to look at something like this off of Whiskers, you can start to piece together, how do I fit in this particular leading causes of death? Another thing you can look at in Whiskers is the numbers of injuries and the associated costs. So you can look at things like falls, unintentional falls. The average is 4.25 million. So looking at all that, I can say, wow, I can now look at stabbings, shootings, firearms, burns, whatever, and I can see breaking out looking under the CDC Whiskers. I can find this information and you can go in there and do all sorts of things with it to mess with it. Another one you can look at would be the NHTSA data, the CODES data. Now, they take our trauma data, they look at police crash reports, EMS and ambulance logs, ED data, and hospital records. And with that, they can pull up data that shows NHTSA CODES data. Look, day of week, we know when that trauma happened. Initial point of impact, we can track that. Occupant role, were they a driver or passengers? Were they restraints were used? What's their sex? What's their age? What was the time of day? Was it a rollover? Was their driver alcohol use? And what they do, they add that with other data from police reports and stuff and they come up with injury information based on all of that data. So, the CODES will look at this and they can also go so far as to say, let's look at by the states and by fatalities looking at drivers, passengers, motorcycles, and by see what percent had seatbelts. We can track all that kind of stuff. We can track all that through the NHTSA CODES data. So, our trauma data is going in and being at the national level being able to pull this information and they can report from NHTSA. Another way this trauma data goes is AAAM. Now, AAAM, they use a global multidisciplinary professionals that include research, the educational programs, and public policy recommendations. But one thing that they do is, this is something that I actually have a picture of this back in the 90s. This was a picture of a patient that we had in our registry and AAAM showed up because they wanted to see, particularly they had, they brought Ford Motor Company with them and they were looking at this particular thing. They wanted, they had gotten all the the data from the registry and the injuries and everything and they were crawling all over this car using laser measurements and it was really cool to sit and watch what they were doing. And what they were looking at was, this is a car that went under a semi-trailer and they were looking at the amount of intrusion. They looked at the injuries and they were able to, from then, what happens is Ford goes back and they help create safer vehicles based off of our trauma data. Think about that. Our trauma data can help car manufacturers create safer things. Think about this, airbags. The new thing are the knee airbags. Why knee airbags? It's because a lot of these injuries, especially to your your tibia, hip fractures, it's when that, it's when your knees eat the dashboard. So now they have knee airbags out. They are currently working, Toyota is working on this, break away brake pedals because a brake pedal has been has been linked to multiple open fractures of the lower extremity by not giving away. So based on our data and the injuries that we that we can show, the car manufacturers are making our vehicles safer. So what and how you document counts. It is crucial that you're accurate with your data and to understand that the data that you put in at your hospital submits makes changes. Now, the data goes way past your hospital. It goes past your hospital, your state, NTDB, TQIP, CDC, NHTSA, AAAM, automotive manufacturers. Legislation is done with using data that we produce. So I just wanted to kind of go through and show you just how far our data really goes. And I want to, since we started out with a bowl, I thought it was only fitting that we end with a bowl as people continue to get injured in innovative ways. So if you have any questions, this is how you can reach me. But I will turn it over to back over and to Dawn to see if they have any questions. Thank you, Mike. That was an awesome presentation. And it's never ceases to amaze me where some of these injuries do come from. And I love the cartoons you had. So we have had a couple questions. And one of them is, of course, you know, we talked about the idea of, you know, the more data we get, the better off we are or so we think. So what's the best use of time for the registry folks? And how do we make sure that we're gathering the right data for us? Well, again, we have to maintain the national, the TQIP, the NTDB data and any state data. Those are required. And if you think about it, and if you've ever been in a position where you get too many data points, and that's why people get in backlog situations so quickly, is because you're reporting on things that no one's asking for that data. So we know that we're being asked by NTDB, by TQIP, and by state what data we need to, they want to see on a state level. So those things all have to go into account. Really anything more than that. And that's where you start getting into trouble of like, why am I spending so much time in this field when I'm never reporting on it? It's not required by anybody, so why do it? It's just, it's just worth something you have to sit down and talk to your staff about. Include your medical director, because a lot of times they don't know all the stuff that we track. They don't. So bring them in and say, this is all the stuff we're tracking. Is there anything that we can get rid of that is not required? And that's going to make your life a lot easier. It's going to free, the registrar is going to feel like, oh, thank God. The one thing that still gets me is a couple of years ago, the NTDB quit having us track all the EMS vital signs and stuff, because they're going to get that from the EMS links at the state. And, but every hospital is still tracking that. And people are going, why are we tracking this when it's not required by NTDB? Well, it's because we have relationships with our local EMS. And so again, this is something that you want to sit down as a team and say, why are we doing this? There should be able to say why and not be told because I said so. Let's talk about this. What's required, what's not. Great. We had another question that came in that said, please clarify, are you suggesting using the hospital's data dictionary as a manual to guide where all data points are located in addition to hospitals, custom data points? Yeah. If you have a lot of people that have the use the NTDB as their base and they simply add any hospital fields, any state fields to that, what's ever in your registry, they just put that field and say, okay, if we're going to track this field, here's where we find that data. And here's where we pull it from, from the EMR. So what that really does is that really helps make sure that there's nobody trying to come up with their own idea of what should go in that field. It says for this field, I'm going to look here, here, and here, and we'll get that information from here in the EMR. That way everybody's on the same page. We know we're all pulling the same data because the rule is if there's five registrars in a room and you ask the question, you'll get five different answers. But if you have that delineated out, there's no difference in opinion. This is what you've decided to do. This is where you want to track it from. This is where you pull it from. So that stops that data that gets kind of murky. So in a similar situation, looking at the external cause, and of course you listed the order in which we should rank them, but say you're getting just into the fall categories. I've seen, likewise, if you've got five registry people, the potential of having five different external cause codes that are all still falls be there. So how can we narrow that down so that we have more similarity? And that's, again, that's where the team involvement is crucial, where I was pulling them together in October, and if we have, if I was able to identify these issues, I say, okay, we have same level falls, but we're putting in three different things. Let's look at what the data dictionary says. What does this fall mean? And so sometimes it won't go into your data dictionary because, like I said, those falls are so different. But as a team, you get together and you decide, okay, if it's the same level fall, we all choose this one because that's the closest to the same level fall under three meters, and so forth. So that's kind of a way to really get that involved. But again, it takes the team to get involved. And I think once you have buy-in where the team can sit down and feel like that their input is given, and it makes for a more cohesive team, and they're more likely to say, hey, I'm part of this. I know what I, I know what we're supposed to be doing now. Let's do it this way. It clears up a lot of problems for them. Sounds great. And of course, I was thinking too, as many hospitals are now transitioning over to a new registry format, that would definitely be a good time to go through and say, what do we need to keep and what can we get rid of? That's exactly right. It's, you know, everybody keeps putting in more and more fields because somebody wants to see it. And it's like, wait a second, why are we doing this? That's just, it's just hard sometimes to sit down and say, okay, we really need to just focus on the national standards, the state standards, and anything local. Everything else is kind of just noise in the background. So it's, that's just so important that now people are going to new things. Now's your time. Hey, let's, let's get into this. If people call and say, hey, is there a way we can take our old reports to this new format? And I'm like, you know, that's like a two-edged sword is that it allows you to create only the reports, you know, you need. But the other side is now I still got to go back and create those reports again, but it allows you to start with a clean slate. And I think it's a good time. If you go to a new registry as a report writer, only do the reports that are required and start. That's that's your basis and start from there. So that's the kind of way I would roll with that one. So, someone else in the group, remember, reminded us to make sure we're reviewing our current procedure codes too, as we're reviewing everything. Exactly. Yeah. What are we, what are we doing? You know? So make sure that those are important. Like I said, the majority of our time as registry professionals is that we spend it in the procedure codes and that's the bane of our existence is the procedure codes. We've got one last question and that is, how can I better utilize other divisions in the hospital to help make the registrar's job easier? That's a great question. In my case, I went and made friends with the head of respiratory, the director of respiratory therapy, because I found out that our trauma surgeons weren't able to tell me when these VAPs occurred. But guess what? The director of respiratory had to submit quarterly reports to the QA department of the hospital on any VAPs. So what I said is, hey, I tell you what, can you send me that report? Because then I could go through and I could catch all those trauma patients that fell through the cracks. And this way I could say, and then if they she needed any reports, I would send her information. So I took that and said, hey, anybody else collecting this information? Let's make friends with them and say, hey, let's exchange reports. You send me this report that covers what I need to see. I can send you stuff when you need it. And so respiratory is really good for the VAPs. I did a lot with the orthopedics people because they have their own ortho registry and I could find out more specific codes or diagnosis from them. So it's that kind of thing of other people track. Radiology has their own registry. Guess what? I don't need to track chest X-rays. Guess what? Someone else does it for me. I just need to call them and get that information. So think about how I can make my life easier. My big thing was respiratory therapy. The director there helped out a ton. Well, we have run out of time. Thank you again, Mike. It has been a wonderful pleasure getting to know you and hear all about data collection and how we can make it work better for our institution. All right. Thanks a lot for having me on here. All right. Thanks. All right. Bye.

trauma data management

Mike Trelo

data collection

trauma centers

data dictionary

hospital guidelines

public safety

data validation

state legislation

trauma registry